MIMO (Multiple-Input and Multiple-Output) is a rising radio technology that uses multiple antennas at both the transmitter and the User Equipment (UE) of a communication system for transmitting data. Advantageously, MIMO achieves the increase of data throughput and link range without the need of additional bandwidth or transmit power.
An advanced MIMO technology called MU-MIMO (Multi-User MIMO), is widely used today to exploit the availability of multiple independent UEs. MU-MIMO further enhances the communication capabilities of each UE.
The performance of MIMO and correspondingly MU-MIMO radio systems relies on a precoding operation which is performed at the transmitter of a communication system. Specifically, precoding is a beamforming technique that supports multi-layer transmission of MIMO systems. In precoding, the multiple streams of the signals are emitted from the transmit antennas of the transmitter with independent and appropriate weighting per each antenna such that the link throughtput is maximized at the receiver (UE) output and the interference between the streams is minimized.
Precoding algorithms used in MU-MIMO are divided into non linear and linear precoding types.
A non linear precoding technique is the so-called Dirty-Paper Coding (DPC) which pre-cancels the interference of the signal transmitted by the transmitter without any power penalties. The transmitter is assumed to know the interference signal regardless of channels state information knowledge. However, the use of this technique is inconvenient according to its cost and complexity and, for such reason, a linear precoding is often preferred.
Indeed, linear precoding can achieve reasonable throughput performance with lower complexity and cost relative to nonlinear precoding. Linear precoding includes zero-forcing (ZF) and Regularized Zero-Forcing (R-ZF) precoding while non linear precoding includes Dirty-Paper Coding (DPC).
Zero-forcing (ZF) technique addresses the drawback of high complexity and cost and performs close to the system capacity. However, it is required that the transmitter knows the downlink channel state information. Accordingly, in the case of limited or inaccurate channel state information, significant loss of the system throughput may occur.
Advantageously to Zero Forcing (ZF), Regularized Zero-Forcing (R-ZF) precoding manages to compensate unfavorable channel conditions by computing a regularization parameter α. This computation is performed at the transmitter for each channel estimation (performed by the receiver) and has to satisfy the transmit sum power constraint. Unfortunately, the computation of a requires knowledge of the downlink channel at the transmitter and thus, in the case of ill-conditioned channel estimates the system throughput is degraded.
In such a context, there is a strong desire to propose a simple solution that provides a satisfactory computation of the regularization parameter α and is independent of the channel estimation.